A New Methodology to Characterise the Radar Bright Band Using Doppler Spectral Moments from Vertically Pointing Radar Observations

The detection and characterisation of the radar Bright Band (BB) are essential for many applications of weather radar quantitative precipitation estimates, such as heavy rainfall surveillance, hydrological modelling or numerical weather prediction data assimilation. This study presents a new techniq...

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Autores principales:	Albert Garcia-Benadí, Joan Bech, Sergi Gonzalez, Mireia Udina, Bernat Codina
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	Doppler radar bright band melting level aliasing Science Q
Acceso en línea:	https://doaj.org/article/610427b25fde49b0aca99447f647361b
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spelling	oai:doaj.org-article:610427b25fde49b0aca99447f647361b2021-11-11T18:53:52ZA New Methodology to Characterise the Radar Bright Band Using Doppler Spectral Moments from Vertically Pointing Radar Observations10.3390/rs132143232072-4292https://doaj.org/article/610427b25fde49b0aca99447f647361b2021-10-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/21/4323https://doaj.org/toc/2072-4292The detection and characterisation of the radar Bright Band (BB) are essential for many applications of weather radar quantitative precipitation estimates, such as heavy rainfall surveillance, hydrological modelling or numerical weather prediction data assimilation. This study presents a new technique to detect the radar BB levels (top, peak and bottom) for Doppler radar spectral moments from the vertically pointing radars applied here to a K-band radar, the MRR-<i>Pro</i> (Micro Rain Radar). The methodology includes signal and noise detection and dealiasing schemes to provide realistic vertical Doppler velocities of precipitating hydrometeors, subsequent calculation of Doppler moments and associated parameters and BB detection and characterisation. Retrieved BB properties are compared with the melting level provided by the MRR-<i>Pro</i> manufacturer software and also with the 0 °C levels for both dry-bulb temperature (freezing level) and wet-bulb temperature from co-located radio soundings in 39 days. In addition, a co-located Parsivel disdrometer is used to analyse the equivalent reflectivity of the lowest radar height bins confirming consistent results of the new signal and noise detection scheme. The processing methodology is coded in a Python program called RaProM-<i>Pro</i> which is freely available in the GitHub repository.Albert Garcia-BenadíJoan BechSergi GonzalezMireia UdinaBernat CodinaMDPI AGarticleDoppler radarbright bandmelting levelaliasingScienceQENRemote Sensing, Vol 13, Iss 4323, p 4323 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Doppler radar bright band melting level aliasing Science Q
spellingShingle	Doppler radar bright band melting level aliasing Science Q Albert Garcia-Benadí Joan Bech Sergi Gonzalez Mireia Udina Bernat Codina A New Methodology to Characterise the Radar Bright Band Using Doppler Spectral Moments from Vertically Pointing Radar Observations
description	The detection and characterisation of the radar Bright Band (BB) are essential for many applications of weather radar quantitative precipitation estimates, such as heavy rainfall surveillance, hydrological modelling or numerical weather prediction data assimilation. This study presents a new technique to detect the radar BB levels (top, peak and bottom) for Doppler radar spectral moments from the vertically pointing radars applied here to a K-band radar, the MRR-<i>Pro</i> (Micro Rain Radar). The methodology includes signal and noise detection and dealiasing schemes to provide realistic vertical Doppler velocities of precipitating hydrometeors, subsequent calculation of Doppler moments and associated parameters and BB detection and characterisation. Retrieved BB properties are compared with the melting level provided by the MRR-<i>Pro</i> manufacturer software and also with the 0 °C levels for both dry-bulb temperature (freezing level) and wet-bulb temperature from co-located radio soundings in 39 days. In addition, a co-located Parsivel disdrometer is used to analyse the equivalent reflectivity of the lowest radar height bins confirming consistent results of the new signal and noise detection scheme. The processing methodology is coded in a Python program called RaProM-<i>Pro</i> which is freely available in the GitHub repository.
format	article
author	Albert Garcia-Benadí Joan Bech Sergi Gonzalez Mireia Udina Bernat Codina
author_facet	Albert Garcia-Benadí Joan Bech Sergi Gonzalez Mireia Udina Bernat Codina
author_sort	Albert Garcia-Benadí
title	A New Methodology to Characterise the Radar Bright Band Using Doppler Spectral Moments from Vertically Pointing Radar Observations
title_short	A New Methodology to Characterise the Radar Bright Band Using Doppler Spectral Moments from Vertically Pointing Radar Observations
title_full	A New Methodology to Characterise the Radar Bright Band Using Doppler Spectral Moments from Vertically Pointing Radar Observations
title_fullStr	A New Methodology to Characterise the Radar Bright Band Using Doppler Spectral Moments from Vertically Pointing Radar Observations
title_full_unstemmed	A New Methodology to Characterise the Radar Bright Band Using Doppler Spectral Moments from Vertically Pointing Radar Observations
title_sort	new methodology to characterise the radar bright band using doppler spectral moments from vertically pointing radar observations
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/610427b25fde49b0aca99447f647361b
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A New Methodology to Characterise the Radar Bright Band Using Doppler Spectral Moments from Vertically Pointing Radar Observations

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